This work presents a modeling approach for estimation of the equivalent greenhouse gas (GHG) emissions of plugin hybrid electric vehicles (PHEVs) for real driving patterns and charging behaviors. In general, modeling of the equivalent GHG for a trip made by a PHEV not only depends on the trip trace in question, but also on the electric range of the vehicle and energy consumption in previous trips since the last charging event. This can significantly increase the necessary computational burden of estimating the GHG emissions using numerical simulation tools, which are already computationally-expensive. The proposed approach allows a trip numerical simulation starting with a fully charged battery to be re-used for GHG estimation of a trip that starts with any initial state of charge by re-allocating the appropriate amount electric energy to an equivalent gas consumption. Thus, the proposed approach allows modeling of many charging behaviors with minimal additional computational effort beyond one numerical simulation for each drive trace. Validation of the proposed approach is established through comparative simulations using 1012 trip traces, where the error in estimated fuel consumption was less than 3% in most simulations. The full set of trip traces in California Household Travel Survey (CHTS) were then analyzed for the two PHEV models (with short and long electric range), for different charging behaviors and grid conditions.